Inventory
Identify AI use cases, owners, purpose, data sources, users, and lifecycle state.
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AI governance entry asset
AI Governance Readiness Kit
Assess whether an organization can approve, govern, and monitor AI use cases without creating unmanaged business, privacy, security, or compliance risk.
Classify
Tier AI risk using sensitivity, decision impact, automation, and oversight requirements.
Control
Confirm data, access, privacy, security, retention, and monitoring controls.
Operate
Use reviews, exceptions, KPIs, and monitoring to keep AI governed after launch.
Interactive readiness assessment
Score the controls that determine AI readiness
Each score creates a practical readiness signal. Executives see risk posture. Practitioners see what must be fixed.
Selected domain
AI Inventory
Weak state
AI use cases are informal, untracked, or owned by individual teams.
Target state
Approved inventory with owners, purpose, data sources, risk tier, and review status.
Executive value
Shows leadership what AI is being used, who owns it, and where risk exists.
Practitioner value
Creates a controlled register of use cases, owners, data sources, users, and lifecycle status.
Readiness result
Foundational AI governance readiness
25% readiness based on the selected controls.
Start with control visibility
Build the AI inventory, assign owners, classify risk, and stop high-impact use cases from moving without review.
AI inventoryUse case ownershipRisk tieringMinimum approval workflow
SVG control model
AI governance control loop
The operating model should connect business demand, governed data, approval evidence, human accountability, and monitoring.
Expandable workflow cards
AI use case review workflow
This is the front-end process that turns AI interest into an accountable review, approval, and monitoring pathway.
Business owner
Submit
Capture purpose, expected users, data sources, AI type, decision impact, and business value.
Governance and risk
Classify
Assign risk tier using sensitivity, automation level, user impact, regulatory exposure, and oversight needs.
Privacy, security, legal, data governance
Review
Validate controls, data usage, access, retention, explainability, human oversight, and exceptions.
Governance council or delegate
Approve
Approve, reject, or approve with conditions. Record owner accountability and monitoring obligations.
Use case owner and governance team
Monitor
Track performance, incidents, exceptions, material changes, and control effectiveness after deployment.
Swimlane visual
Who participates in AI governance review
Executives need accountability. Practitioners need role clarity. This swimlane keeps both audiences aligned.
Role
Submit
Classify
Review
Approve
Monitor
Business Owner
Defines business purpose
Confirms decision impact
Accepts control obligations
Owns deployment accountability
Confirms ongoing business fit
Data Governance
Checks inventory fields
Reviews data sensitivity
Validates metadata and lineage
Sets governance conditions
Tracks governance KPIs
Security and Privacy
Reviews data exposure
Assesses privacy impact
Confirms safeguards
Approves control evidence
Monitors incidents and exceptions
Platform Team
Confirms platform path
Reviews access model
Implements controls
Supports deployment
Maintains technical monitoring
Decision point
Use this readiness kit before AI becomes shadow risk.
The next valuable conversation is not about buying a tool. It is about defining the intake process, risk model, accountability structure, and monitoring rhythm.